



'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowercase      			=	logging.get_logger(__name__)



def        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[int]	):







					"""simple docstring"""






					if isinstance(a__					,   (list, tuple)	) and isinstance(videos[0]					,   (list, tuple)	) and is_valid_image(videos[0][0]	):
										return videos

					elif isinstance(a__					,   (list, tuple)	) and is_valid_image(videos[0]	):
										return [videos]

					elif is_valid_image(a__	):
										return [[videos]]

					raise ValueError(f'Could not make batched video from {videos}'	)






class    __lowerCamelCase							( a_   ):







	'''simple docstring'''
	snake_case__		:   str       =				['''pixel_values''']






	def __init__(      self      ,     a__ = True      ,     a__ = None      ,     a__ = PILImageResampling.BILINEAR      ,     a__ = True      ,     a__ = None      ,     a__ = True      ,     a__ = 1 / 255      ,     a__ = True      ,     a__ = None      ,     a__ = None      ,     **a__      ,     ):
						super().__init__(**_lowercase					)
						__SCREAMING_SNAKE_CASE				:    int						    =							size if size is not None else {"""shortest_edge""": 224}
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							get_size_dict(_lowercase      ,     default_to_square=_lowercase					)
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						__SCREAMING_SNAKE_CASE				:    str						    =							get_size_dict(_lowercase      ,     param_name="crop_size"					)

						__SCREAMING_SNAKE_CASE				:    int						    =							do_resize
						__SCREAMING_SNAKE_CASE				:    int						    =							size
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							do_center_crop
						__SCREAMING_SNAKE_CASE				:    int						    =							crop_size
						__SCREAMING_SNAKE_CASE				:    Any						    =							resample
						__SCREAMING_SNAKE_CASE				:    Any						    =							do_rescale
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							rescale_factor
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							do_normalize
						__SCREAMING_SNAKE_CASE				:    Any						    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE				:    str						    =							image_std if image_std is not None else IMAGENET_STANDARD_STD






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = PILImageResampling.BILINEAR      ,     a__ = None      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    int						    =							get_size_dict(_lowercase      ,     default_to_square=_lowercase					)
						if "shortest_edge" in size:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							get_resize_output_image_size(_lowercase      ,     size["shortest_edge"]      ,     default_to_square=_lowercase					)
						elif "height" in size and "width" in size:
											__SCREAMING_SNAKE_CASE				:    str						    =							(size["""height"""], size["""width"""])
						else:
											raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
						return resize(_lowercase      ,     size=_lowercase      ,     resample=_lowercase      ,     data_format=_lowercase      ,     **_lowercase					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Any						    =							get_size_dict(_lowercase					)
						if "height" not in size or "width" not in size:
											raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'					)
						return center_crop(_lowercase      ,     size=(size["height"], size["width"])      ,     data_format=_lowercase      ,     **_lowercase					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						return rescale(_lowercase      ,     scale=_lowercase      ,     data_format=_lowercase      ,     **_lowercase					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						return normalize(_lowercase      ,     mean=_lowercase      ,     std=_lowercase      ,     data_format=_lowercase      ,     **_lowercase					)






	def  a_					(      self      ,     a__      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = ChannelDimension.FIRST      ,     ):
						if do_resize and size is None or resample is None:
											raise ValueError("Size and resample must be specified if do_resize is True."					)

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."					)

						if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."					)

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError("Image mean and std must be specified if do_normalize is True."					)

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE				:    str						    =							to_numpy_array(_lowercase					)

						if do_resize:
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							self.resize(image=_lowercase      ,     size=_lowercase      ,     resample=_lowercase					)

						if do_center_crop:
											__SCREAMING_SNAKE_CASE				:    int						    =							self.center_crop(_lowercase      ,     size=_lowercase					)

						if do_rescale:
											__SCREAMING_SNAKE_CASE				:    Any						    =							self.rescale(image=_lowercase      ,     scale=_lowercase					)

						if do_normalize:
											__SCREAMING_SNAKE_CASE				:    Dict						    =							self.normalize(image=_lowercase      ,     mean=_lowercase      ,     std=_lowercase					)

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							to_channel_dimension_format(_lowercase      ,     _lowercase					)
						return image






	def  a_					(      self      ,     a__      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = ChannelDimension.FIRST      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE				:    str						    =							do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE				:    int						    =							do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE				:    str						    =							rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE				:    Any						    =							do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE				:    Dict						    =							image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							size if size is not None else self.size
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							get_size_dict(_lowercase      ,     default_to_square=_lowercase					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							get_size_dict(_lowercase      ,     param_name="crop_size"					)

						if not valid_images(_lowercase					):
											raise ValueError(
											    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
											    "torch.Tensor, tf.Tensor or jax.ndarray."					)

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							make_batched(_lowercase					)

						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							[
						    [
						        self._preprocess_image(
						            image=_lowercase      ,     do_resize=_lowercase      ,     size=_lowercase      ,     resample=_lowercase      ,     do_center_crop=_lowercase      ,     crop_size=_lowercase      ,     do_rescale=_lowercase      ,     rescale_factor=_lowercase      ,     do_normalize=_lowercase      ,     image_mean=_lowercase      ,     image_std=_lowercase      ,     data_format=_lowercase      ,     )
						        for img in video
						    ]
						    for video in videos
						]

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							{"""pixel_values""": videos}
						return BatchFeature(data=_lowercase      ,     tensor_type=_lowercase					)





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


